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tests/semantic_segmentation/test_LogisticRegressionSegmentor.py
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| import numpy as np | ||
| import pytest | ||
| from sklearn.exceptions import NotFittedError | ||
| from patsemb.semantic_segmentation import LogisticRegressionSegmentor | ||
| from patsemb.pattern_based_embedding import PatternBasedEmbedder | ||
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| @pytest.fixture | ||
| def pattern_based_embedding() -> np.ndarray: | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| return PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
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| class TestLogisticRegressionSegmentor: | ||
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| def test_initialization_n_segments(self): | ||
| clf = LogisticRegressionSegmentor() | ||
| assert clf.n_segments == [2, 3, 4, 5, 6, 7, 8, 9] | ||
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| clf = LogisticRegressionSegmentor(n_segments=[2, 3, 4, 5]) | ||
| assert clf.n_segments == [2, 3, 4, 5] | ||
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| clf = LogisticRegressionSegmentor(n_segments=4) | ||
| assert clf.n_segments == [4] | ||
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| def test_initialization_n_jobs(self): | ||
| clf = LogisticRegressionSegmentor() | ||
| assert clf.n_jobs == 1 | ||
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| clf = LogisticRegressionSegmentor(n_jobs=4) | ||
| assert clf.n_jobs == 4 | ||
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| def test_initialization_kwargs(self): | ||
| clf = LogisticRegressionSegmentor(penalty='l2', tol=1e-4, init='random', max_iter=50) | ||
| assert 'tol' in clf.k_means_kwargs | ||
| assert 'init' in clf.k_means_kwargs | ||
| assert 'max_iter' in clf.k_means_kwargs | ||
| assert len(clf.k_means_kwargs) == 3 | ||
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| assert 'penalty' in clf.logistic_regression_kwargs | ||
| assert 'tol' in clf.logistic_regression_kwargs | ||
| assert 'max_iter' in clf.logistic_regression_kwargs | ||
| assert len(clf.logistic_regression_kwargs) == 3 | ||
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| def test_initialization_n_clusters(self): | ||
| with pytest.raises(TypeError): | ||
| LogisticRegressionSegmentor(n_clusters=5) | ||
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| def test_initialization_additional_args(self): | ||
| with pytest.raises(TypeError): | ||
| LogisticRegressionSegmentor(something_invalid=0) | ||
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| def test_fit(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| clf = LogisticRegressionSegmentor() | ||
| assert clf.fit(pattern_based_embedding) == clf | ||
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| def test_predict_proba(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| clf = LogisticRegressionSegmentor() | ||
| clf.fit(pattern_based_embedding) | ||
| pred = clf.predict_proba(pattern_based_embedding) | ||
| assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
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| def test_fit_predict_proba(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| pred = LogisticRegressionSegmentor().fit_predict_proba(pattern_based_embedding) | ||
| assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
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| def test_fit_predict_proba_one_n_segment(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| pred = LogisticRegressionSegmentor(n_segments=3).fit_predict_proba(pattern_based_embedding) | ||
| assert pred.shape == (pattern_based_embedding.shape[1], 3) | ||
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| def test_fit_predict_proba_multiple_jobs(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| pred = LogisticRegressionSegmentor(n_jobs=4).fit_predict_proba(pattern_based_embedding) | ||
| assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
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| def test_predict_proba_not_fitted(self): | ||
| univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| with pytest.raises(NotFittedError): | ||
| LogisticRegressionSegmentor().predict_proba(pattern_based_embedding) | ||
| # import numpy as np | ||
| # import pytest | ||
| # from sklearn.exceptions import NotFittedError | ||
| # from patsemb.semantic_segmentation import LogisticRegressionSegmentor | ||
| # from patsemb.pattern_based_embedding import PatternBasedEmbedder | ||
| # | ||
| # | ||
| # @pytest.fixture | ||
| # def pattern_based_embedding() -> np.ndarray: | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # return PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # | ||
| # | ||
| # class TestLogisticRegressionSegmentor: | ||
| # | ||
| # def test_initialization_n_segments(self): | ||
| # clf = LogisticRegressionSegmentor() | ||
| # assert clf.n_segments == [2, 3, 4, 5, 6, 7, 8, 9] | ||
| # | ||
| # clf = LogisticRegressionSegmentor(n_segments=[2, 3, 4, 5]) | ||
| # assert clf.n_segments == [2, 3, 4, 5] | ||
| # | ||
| # clf = LogisticRegressionSegmentor(n_segments=4) | ||
| # assert clf.n_segments == [4] | ||
| # | ||
| # def test_initialization_n_jobs(self): | ||
| # clf = LogisticRegressionSegmentor() | ||
| # assert clf.n_jobs == 1 | ||
| # | ||
| # clf = LogisticRegressionSegmentor(n_jobs=4) | ||
| # assert clf.n_jobs == 4 | ||
| # | ||
| # def test_initialization_kwargs(self): | ||
| # clf = LogisticRegressionSegmentor(penalty='l2', tol=1e-4, init='random', max_iter=50) | ||
| # assert 'tol' in clf.k_means_kwargs | ||
| # assert 'init' in clf.k_means_kwargs | ||
| # assert 'max_iter' in clf.k_means_kwargs | ||
| # assert len(clf.k_means_kwargs) == 3 | ||
| # | ||
| # assert 'penalty' in clf.logistic_regression_kwargs | ||
| # assert 'tol' in clf.logistic_regression_kwargs | ||
| # assert 'max_iter' in clf.logistic_regression_kwargs | ||
| # assert len(clf.logistic_regression_kwargs) == 3 | ||
| # | ||
| # def test_initialization_n_clusters(self): | ||
| # with pytest.raises(TypeError): | ||
| # LogisticRegressionSegmentor(n_clusters=5) | ||
| # | ||
| # def test_initialization_additional_args(self): | ||
| # with pytest.raises(TypeError): | ||
| # LogisticRegressionSegmentor(something_invalid=0) | ||
| # | ||
| # def test_fit(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # clf = LogisticRegressionSegmentor() | ||
| # assert clf.fit(pattern_based_embedding) == clf | ||
| # | ||
| # def test_predict_proba(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # clf = LogisticRegressionSegmentor() | ||
| # clf.fit(pattern_based_embedding) | ||
| # pred = clf.predict_proba(pattern_based_embedding) | ||
| # assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
| # | ||
| # def test_fit_predict_proba(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # pred = LogisticRegressionSegmentor().fit_predict_proba(pattern_based_embedding) | ||
| # assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
| # | ||
| # def test_fit_predict_proba_one_n_segment(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # pred = LogisticRegressionSegmentor(n_segments=3).fit_predict_proba(pattern_based_embedding) | ||
| # assert pred.shape == (pattern_based_embedding.shape[1], 3) | ||
| # | ||
| # def test_fit_predict_proba_multiple_jobs(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # pred = LogisticRegressionSegmentor(n_jobs=4).fit_predict_proba(pattern_based_embedding) | ||
| # assert pred.shape[0] == pattern_based_embedding.shape[1] | ||
| # | ||
| # def test_predict_proba_not_fitted(self): | ||
| # univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000) | ||
| # pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series) | ||
| # with pytest.raises(NotFittedError): | ||
| # LogisticRegressionSegmentor().predict_proba(pattern_based_embedding) |